1. Field of the Invention
The invention relates generally to solving classification problems. More specifically, the invention addresses the use of a support vector machine to solve classification problems with sample data having a large number of correlated features that can be grouped together, such as in medical image analysis.
2. Description of the Prior Art
The original linear support vector machine (SVM) aims to find a hyperplane that separates a collection of data points. Since it was proposed, the SVM has been widely used for binary classifications, such as texture classification, gene expression data analysis and face recognition. Mathematically, given a set of samples {xi}i=1n in p-dimensional space and each xi attached with a label yi∈{+1, −1}, the linear SVM learns a hyperplane (w*)Tx+b*=0 from the training samples. A new data point x can be categorized into the “+1” or “−1” class by inspecting the sign of (w*)Tx+b*.
The binary-class SVM (B-SVM) has been generalized to multicategory classifications to tackle problems where the data points belong to more than two classes. The initially proposed multi-class SVM (M-SVM) methods construct several binary classifiers, such as “one-against-all,” “one-against-one,” and “directed acyclic graph SVM.” These M-SVMs may suffer from data imbalance; namely, some classes have far fewer data points than others, which can result in inaccurate predictions. One alternative is to put all the data points together in one model, which results in the so-called “all-together” M-SVMs. The “all-together” M-SVMs train multi-classifiers by considering a single large optimization problem.
A need exists, however, for techniques to assure that both the binary and the multiclass methods converge linearly, and to reduce the run-time of the models.
Medical images such as X-rays images, CAT scans and MRI images provide physicians with non-intrusive techniques for the diagnosis of a wide variety of medical conditions. The analysis of those images often involves the classification of the image as indicating one of two medical conditions, or as indicating one of a larger number of medical conditions.
Manual analysis of medical images is complex and time consuming, and requires experts with extensive experience. For those reasons, computer learning techniques and classification methods have been used to identify medical conditions using images. The classification methods, however, suffer from difficulties with convergence and with long run times, because of the extremely high dimensionality that is typical of medical image classification problems. There is therefore a need for medical image analysis techniques that assure linear convergence and reduce the run-time of the models.